semforest.control

print.semforest.control

num.trees

Sampling procedure. Can be subsample or bootstrap.

sampling

A SEM Tree control object. Will be generated by default.

control

Number of subsampled covariates at each node.

mtry

A SEM Forest control object to tune parameters of the forest learning
algorithm.

SEM Trees and SEM Forests -- an extension of model-based decision
trees and forests to Structural Equation Models (SEM). SEM trees hierarchically
split empirical data into homogeneous groups each sharing similar data patterns
with respect to a SEM by recursively selecting optimal predictors of these
differences. SEM forests are an extension of SEM trees. They are ensembles of
SEM trees each built on a random sample of the original data. By aggregating
over a forest, we obtain measures of variable importance that are more robust
than measures from single trees. A description of the method was published by
Brandmaier, von Oertzen, McArdle, & Lindenberger (2013; <doi:10.1037/a0030001>)
and Arnold, Voelkle, & Brandmaier (2020; <doi:10.3389/fpsyg.2020.564403>).

semforest.control: SEM Forest Control Object

Description

Usage

Arguments

References